The production of an annual statistical report generally falls under the responsibility of the government office in charge of fisheries (e.g. a Fisheries Department or Ministry). Often, a national fishery research institute may be deeply involved in collecting statistics, particularly if it belongs to the ministry responsible for fisheries. The institute will also use the statistics for stock assessments, elaboration of scientific advice and analyses of management performance, ensuring some sort of quality control. Other fisheries-related data (e.g. data on demographics, infrastructure, imports-exports, prices, encroachments) may be collected by other departments or ministries such as the Coast Guards (Navy), the meteorological office, the Treasury or the private sector (e.g. development banks), creating a need for coordination or integration of data systems.
Need for basic fishery statistics
Collection of basic data on fishers, catches, fishing effort, prices, values and other related information such as size at capture and length frequencies, is fundamental for most activities related to policy, planning and management of fisheries and aquaculture.
Occasional censuses (frame surveys) and sample-based fishery surveys conducted on a regular basis ought to be viewed not as an end in themselves but as an important source of fishery information for a wide range of activities such as:
- Estimated total production (or total catch) Combined with data on imports and exports, it constitutes the basis for quotas regulations and for calculating per capita consumption of fish, which is subsequently used in the formulation of food balance sheets. It is also the basis for calculating the Catch-Per-Unit-Effort (CPUE) often used as a proxy for resource abundance. Biological information such as catch by size or age-groups is a basic need for studying gear selectivity, growth, as well as natural and fishing mortality, which can be further used in modeling. It is also usually required to collect data on catch, landings and discards.
- Estimated total value of fish production An important element in assessing the relative importance of the fishing industry within the national economy and in evaluating its economic performance.
- Prices at landing Combined with data on operational costs, they can provide indices of fleet economic performance. Time series of prices are used in socio-economic or bio-economic studies.
- Fishing effort Together with catch constitute the basic elements in the formulation of indices of abundance (CPUE). Combined with catch data, it can be used to estimate stocks potential and in simulation models to design management scenarios. As time series, it provides information on the development of the fisheries and the evolution of fishing capacity.
- Numbers of fishers By type, this provides valuable information regarding trends in human involvement in the fishing industry and lead to inferences on the social and economic importance of the sector.
The quality of the data collected impacts directly on the quality of the analyses made on them including the indicators, stock assessments and forecasts elaborated using them. In addition, the quality of the national data collection system will influence the ultimate quality of the data collected and compiled in other statistical systems, at regional level (i.e. in regional fishery commissions) or global level (e.g. at FAO).
Human and financial resources
Availability of sufficient and adequate human resources is often one of the major constraints in the implementation of medium and large-scale fishery surveys operated on a regular basis, particularly in cases of fishery administrations with limited budget allocated for data collection programmes. The backbone of a data collection programme is the team of field data recorders and supervisors. Well-trained and motivated data recorders is the principal concern of fishery statistical units since it is through them that primary information flows from fishing sites and markets up to the offices responsible for data processing and analysis. Consequently the quality and utility of the produced statistics is a direct function of the effectiveness and timeliness of field operations.
Mobility of data recorders - the ability to visit as many locations as possible during an allocated time - also affects the quantity of collected data as well as their representativeness. Lack of transportation means reduced statistical coverage and increases the risk for biased data, since data collection is then always conducted at the same few locations. As well, mobility is dependent on supervisory functions, the lack of which would leave data recorders on their own and without supervision and guidance.
Mobilization of human resources not necessarily regular staff of fishery administrations is often a good approach for obtaining information that does not require highly skilled personnel. For instance, data on the level of activity of fishing units (a basic parameter in estimating fishing effort), is sometimes obtained directly from the fishers themselves; students or scouts living in fishing sites can also act as data recorders thus significantly increasing the sample size at a reasonably low cost.
When established, statistical systems should be optimized in order to efficiently use the scarce resources available. Optimization is a constant concern because, without special attention, performance of statistical systems tends to degrade with time.
Experience shows that, in general, design and implementation of sample-based fishery surveys make little or no use of statistical indicators concerning sampling requirements that would guarantee an acceptable level of reliability for the estimated population parameters. On several occasions, statistical developers tended to "over-sample", despite the fact that a priori guidance on sample size requirements was feasible in most cases. Lack of well-defined and cost-effective sampling schemes tends to increase the size and complexity of field operations without the visible benefits in accuracy which, in turn, directly impacts on the logistics aspects of data collection and data management procedures.
Excessive stratification of the target population without due consideration to its impact on sampling requirements can be another limiting factor. Refined stratification certainly improves the homogeneity aspects of a population but has serious impact on sampling effort and survey cost. This factor is at times overlooked by statistical developers who continue to apply old sampling schemes proportionally to the size of newly created stratified populations, maintaining the same total number of samples collected over the reference period. According to basic sampling theory, this approach is not appropriate and safe sample sizes ought to be reviewed and adjusted after stratification.
Lack of sufficient and appropriate data processing tools and methods are often a negative factor in the operations of fishery statistical programmes. With the proliferation of microcomputers and increased computer literacy among data producers and users, computer systems (of varying sophistication and power) have, in fact, become an inseparable component of fishery statistical systems but, with few exceptions, they tend to be fragmented, inflexible and heavily centralized. Lack of flexibility and robustness implies frequent interventions to the software thus increasing the chances of undetected programming faults, while the lack of a modular system structure creates processing bottlenecks and deprives decentralized offices from the benefit of locally processing and analyzing their own data.
One of the key concerns in data collection is the sustainability of the adopted system, in terms of human, financial and computational resources as well as the ability to adapt to changing needs. This implies first of all that the data produced are effectively used, both for planning and management, creating a demand to "stimulate" their collection and elaboration and guarantee their long-term relevance. Sustainability also implies that an adequate compromise is found between precision and costs; theory and pragmatism; sophistication and simplicity; performance and resilience. The system should focus on the most important data. Data collection needs to be as "painless" as possible. Although in most fishery surveys the estimation process is fairly simple from the mathematical standpoint, there are other statistical and non-statistical aspects requiring well-defined, robust, modular and flexible computer systems. Samples collected from the field would have to be classified and stored. Estimation of population parameters from the collected data ought to be as automated as possible to avoid lengthy, risky and routinely performed manual computations. Finally, automatic preparation of statistical reports, indicators and diagnostics is essential in identifying problem areas and deciding as to the type of corrective action required.
Standardized computer tools
Most data collection systems have many common characteristics, irrespective of their environment and individual methodological and operational aspects. Starting from this concept FAO's Fishery Information, Data and Statistics Unit has developed a family of standardized statistical approaches which have been compiled into software (Artfish) aimed at facilitating the design and implementation of shore-based fishery surveys on fish production and values.
Despite common needs and problems, most countries have a statistical system with a long history including an administrative structure, trained staff, fixed formal requirements, where restructuring requires a customized approach taking this history into account. This approach, as applied by FAO, is comprehensive, demand-oriented and institution-centred. It addresses both the needs of:
- the main statistical office;
- the peripheral staff complement;
- the related research and analysis;
- the institutional framework.
Its implementation requires:
- identifying the functions required from the system and from the administration managing it;
- distinguishing the related technical requirements (norms and standards, types of reports, periodicity, "clients", etc.);
- accounting for historical developments, existing capacity and tools;
- recognizing main problems (data quality, timeliness, information technology).
It also requires a range of expertise including statistical systems design, fisheries assessment and management, fishery statistics, statistical analysis and economics, use of and training on statistical packages and data presentation, computer programming and software development, information technology and connectivity, regional legal instruments and requirements. After the rehabilitation, close interaction with the staff must be maintained to provide assistance, including through remote online support.
The outputs of the process usually include: a computerised fishery census (and a national fishing register), a computerised methodology to support the management of fishing licences and related authorizations and certificates, fleet monitoring tools, a computerized statistical year-book, routines for the production of periodic electronic or paper reports, a catch and effort assessment survey as well as capacity building components.
FAO has already developed a set of computer applications with an open architecture enabling fast, stable and error-free generation of national standalone application (derived systems) incorporated into wider systems, or enabling the incorporation of other or new applications.
Training in statistics at all levels (data producers and users) and on computers (data operators and analysts) is of primary importance for adequate monitoring, co-ordination, corrective action and adaptation, as well as evaluation, in field and office operations. Training in survey design provided by FAO during the last few years has been conducted mainly through the Artplan module of the Artfish software. Artplan operates with empirical parameters and makes maximum use of existing knowledge regarding fishing operations and patterns. Its functions include:
- Establishing working frames with sites, boat/gear types and strata;
- Definition of fishing patterns;
- Simulating fishing operations;
- Generating "pseudo-census" information;
- Evaluating alternative sampling scenarios for cost-effectiveness.
Storage and processing
Collected data need to be safely stored and efficiently processed to generate the required estimates and reports. The Artbasic module of Artfish can be used for the storage and processing of basic data on catches and values and fishing effort. It operates on standard classifications, frame survey data and samples on catches, fishing effort, prices and values. Results consist of monthly estimates of fish production and values by species within the logical context of a calendar month (or a sub-period), a stratum and a specific boat/gear type. All Artbasic estimates are presented with the associated statistical diagnostics (such as variability explained in both space and time, expected accuracy level). Artbasic is usually operated on a decentralized basis thus offering research and reporting services nearer to the data sources. Artfish can also be used for reporting through its Artser module in which estimates from Artbasic are automatically gathered and formatted under an integrated database structure that allows for flexible and user-friendly data screening and extraction, data grouping, reporting and plotting.
To help meet these data needs, FAO has been assisting countries in upgrading their data collection, processing and reporting capabilities. Technical assistance at national and regional level is a significant component of the work programme of FAO's technical units responsible for fishery statistical development and involves both normative and field programme activities. Outputs of the normative activities include technical documents on statistical methodology and guidelines for data collection, while field programme activities involve project formulation and implementation, technical backstopping and organization of training courses and workshops.